Ultra High-Resolution Image Inpainting with Patch-Based Content Consistency Adapter

• URL: http://arxiv.org/abs/2510.13419v1
• Date: Wed, 15 Oct 2025 11:18:24 GMT
• Title: Ultra High-Resolution Image Inpainting with Patch-Based Content Consistency Adapter
• Authors: Jianhui Zhang, Sheng Cheng, Qirui Sun, Jia Liu, Wang Luyang, Chaoyu Feng, Chen Fang, Lei Lei, Jue Wang, Shuaicheng Liu,
• Abstract summary: We present Patch-Adapter, an effective framework for high-resolution text-guided image inpainting.<n>Our approach achieves 4K+ resolution while maintaining precise content consistency and prompt alignment.<n> Experiments demonstrate that Patch-Adapter not only resolves artifacts common in large-scale inpainting but also achieves state-of-the-art performance.
• Score: 47.512192547392026
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In this work, we present Patch-Adapter, an effective framework for high-resolution text-guided image inpainting. Unlike existing methods limited to lower resolutions, our approach achieves 4K+ resolution while maintaining precise content consistency and prompt alignment, two critical challenges in image inpainting that intensify with increasing resolution and texture complexity. Patch-Adapter leverages a two-stage adapter architecture to scale the diffusion model's resolution from 1K to 4K+ without requiring structural overhauls: (1) Dual Context Adapter learns coherence between masked and unmasked regions at reduced resolutions to establish global structural consistency; and (2) Reference Patch Adapter implements a patch-level attention mechanism for full-resolution inpainting, preserving local detail fidelity through adaptive feature fusion. This dual-stage architecture uniquely addresses the scalability gap in high-resolution inpainting by decoupling global semantics from localized refinement. Experiments demonstrate that Patch-Adapter not only resolves artifacts common in large-scale inpainting but also achieves state-of-the-art performance on the OpenImages and Photo-Concept-Bucket datasets, outperforming existing methods in both perceptual quality and text-prompt adherence.

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